Integrating multiple distributed data processing servers with different data partitioning and routing mechanisms, resource sharing policies and lifecycles into a single process

ABSTRACT

A method is provided for consistent data processing by first and second distributed processing systems having different data partitioning and routing mechanisms such that the first system is without states and the second system is with states. The method includes dividing data in each system into a same number of partitions based on a same key and a same hash function. The method includes mapping partitions between the systems in a one-to-one mapping. The mapping step includes (i) checking if a partition in the first system is mapped to a partition in the second system; and (ii) calculating a partition ID based on the hash function and a total number of partitions, and dynamically mapping the partition in the first system to the partition in the second system, responsive to the partition in the first system being unmapped to the partition in the second system.

BACKGROUND Technical Field

The present invention relates generally to information processing and,in particular, to integrating multiple distributed data processingservers with different data partitioning and routing mechanisms,resource sharing policies and lifecycles into a single process.

Description of the Related Art

A new stateful streaming data processing engine is being developed forIBM Watson® Internet of Things for Automotive (IoT4A) with ApacheSpark®, Apache Kafka®, and the IBM Agent Framework for DataGrid (AFDG).Spark® is a large scale distributed data processing system. Kafka® is apublish/subscribe based distributed messaging platform. AFDG is an agentbased real time distributed data processing system.

Current IoT4A already integrates AFDG and many of its applications arewritten with AFDG. As a new member of IoT4A family, the new statefulstreaming data processing engine is expected to work with the existingapplications nicely.

The new stateful streaming data processing engine also needs to supportfast access to local data partitions. However, the use of an externaldata store cannot meet the performance requirement.

Moreover, the preceding involves different routing mechanisms. Forexample, Spark® Streaming integration for Kafka® supports Kafka®partition based routing, whereas AFDG supports AFDG region basedrouting, thus being inconsistent.

Also, the preceding (Spark® and AFDG) is designed as standalone servers.While it is desirable to run Spark® and AFDG as a single process forfast data access, both Spark® workers and AFDG servers are designed asstandalone servers.

Additionally, their lifecycles are also different. Hence, there is aneed for a way to integrate multiple distributed data processing systemswith different data partitioning and routing mechanisms, resourcesharing policies and lifecycles into a single process.

SUMMARY

According to an aspect of the present invention, a computer-implementedmethod is provided for enabling consistent data processing by first andsecond distributed processing systems having different data partitioningand routing mechanisms such that the first distributed processing systemis without states and the second distributed processing system is withstates. The method includes dividing data in each of the distributedprocessing systems into a same number of partitions based on a same keyand a same hash function. The method further includes mapping partitionsbetween the distributed processing systems in a one-to-one mapping. Themapping step includes (i) checking if a partition in the firstdistributed processing system is already mapped to a partition in thesecond distributed processing system, responsive to receiving a data setfor processing by the first distributed processing system; and (ii)calculating a partition ID based on the hash function and a total numberof partitions, and dynamically mapping the partition in the firstdistributed processing system to the partition in the second distributedprocessing system, responsive to the partition in the first distributedprocessing system being unmapped to the partition in the seconddistributed processing system.

According to another aspect of the present invention, a computer programproduct is provided for enabling consistent data processing by first andsecond distributed processing systems having different data partitioningand routing mechanisms such that the first distributed processing systemis without states and the second distributed processing system is withstates. The computer program product includes a non-transitory computerreadable storage medium having program instructions embodied therewith.The program instructions are executable by a computer to cause thecomputer to perform a method. The method includes dividing data in eachof the distributed processing systems into a same number of partitionsbased on a same key and a same hash function. The method furtherincludes mapping partitions between the distributed processing systemsin a one-to-one mapping. The mapping step includes (i) checking if apartition in the first distributed processing system is already mappedto a partition in the second distributed processing system, responsiveto receiving a data set for processing by the first distributedprocessing system; and (ii) calculating a partition ID based on the hashfunction and a total number of partitions, and dynamically mapping thepartition in the first distributed processing system to the partition inthe second distributed processing system, responsive to the partition inthe first distributed processing system being unmapped to the partitionin the second distributed processing system.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The following description will provide details of preferred embodimentswith reference to the following figures wherein:

FIG. 1 is a block diagram showing an exemplary environment to which thepresent invention can be applied, in accordance with an embodiment ofthe present invention;

FIG. 2 shows an exemplary method for integrating multiple distributeddata processing systems with different data partitioning and routingmechanisms, resource sharing policies, and lifecycles into a singleprocess, in accordance with an embodiment of the present invention;

FIG. 3 shows another exemplary method for integrating multipledistributed data processing systems with different data partitioning androuting mechanisms, resource sharing policies, and lifecycles into asingle process, in accordance with an embodiment of the presentinvention;

FIG. 4 shows a cloud computing environment, in accordance with anembodiment of the present invention; and

FIG. 5 shows abstraction model layers, in accordance with an embodimentof the present invention.

DETAILED DESCRIPTION

The present invention is directed to integrating multiple distributeddata processing servers with different data partitioning and routingmechanisms, resource sharing policies and lifecycles into a singleprocess.

In an embodiment, the present invention provides consistent dataprocessing for multiple distributed data processing systems havingdifferent data partitioning and routing mechanisms, by dynamicallymapping their data partitions (or regions).

In an embodiment, multiple servers are enabled to have differentresource sharing policies and lifecycles integrated into a singleprocess, by devising the same at the time of operation.

FIG. 1 is a block diagram showing an exemplary environment 100 to whichthe present invention can be applied, in accordance with an embodimentof the present invention. The environment 100 is representative of adistributed computer environment to which the present invention can beapplied. The elements shown relative to FIG. 1 are set forth for thesake of illustration. However, it is to be appreciated that the presentinvention can be applied to other configurations and other operationalenvironments as readily contemplated by one of ordinary skill in the artgiven the teachings of the present invention provided herein, whilemaintaining the spirit of the present invention.

The environment 100 includes a Spark® distributed data processing system(hereinafter interchangeably referred to as the “Spark® system” inshort) 110, a Kafka® distributed messaging platform 150, and the IBMAgent Framework for DataGrid (AFDG) 170.

The elements 110, 150, and 170 can be configured to implement a statefulstreaming data processing engine for IoT4A. In such a case, the Spark®distributed data processing system 110 can be used as a streamingprocessing engine for the stateful streaming processing engine. TheKafka® distributed messaging platform 150 can be used as a dynamicrouting mechanism for the stateful streaming processing engine. The AFDG170 can be used as an in-memory data store for the stateful streamingprocessing engine. The Spark® distributed data processing system 110 andthe Kafka® distributed messaging platform 150 can be considered to forma Spark Streaming integration for Kafka® distributed data processingsystem 186.

While a Spark® distributed data processing system, a Kafka® distributedmessaging platform, and an AFDG are described herein for the sake ofillustration, it is to be appreciated that the present invention is notlimited to solely the preceding types of distributed systems anddistributed platforms and, thus, other types of distributed systems anddistributed platforms can also be used in accordance with the teachingsof the present invention, while maintaining the spirit of the presentinvention. For example, other embodiments of the present invention caninvolve at least one distributed data processing system without states(similar to Spark® Streaming integration for Kafka®), while at leastanother distributed data processing system is with states (similar toAFDG). These and other system types to which the present invention canbe applied are readily appreciated by one of ordinary skill in the art,given the teachings of the present invention provided herein.

The Spark® distributed data processing system 110 includes a Spark®cluster 111. A master server 190 is part of the Spark cluster 111. TheSpark cluster 111 has a set of worker processes 112 connected to themaster 190. The set of worker processes 112 includes used workerprocesses 112A and unused worker processes 112B. In an embodiment, eachof the used worker processes 112A has a single executor thread 113 and aregion (which may also be interchangeably referred to as a “partition”)114. The used worker processes 112A are being currently used to executean application 115. The executors 113 are threads that run computationsfor the application 115. The regions 114 store data for the application115. A Spark® application (e.g., application 115) runs as a set ofworker processes (e.g., used worker processes 112A) on a Spark® cluster(e.g., Spark® cluster 111). A Spark® application includes a driverprogram and executors (e.g., executors 113). A task is a unit of worksent to an executor 113.

The Kafka® distributed messaging platform 150 includes a Kafka® cluster151 having a Kafka broker 152. The Kafka® cluster 151 stores streams ofrecords in categories called topics. Each topic is stored in a set ofpartitions 153. Each record consists of a key, a value, and a timestamp.The Kafka® broker 152 is used to replicate messages. Kafka® includes thefollowing four major APIs:

-   (1) Producer API—permits the applications to publish streams of    records;-   (2) Consumer API—permits the application to subscribe to the topics    and processes the stream of records.-   (3) Streams API—converts the input streams to output and produces    the result.-   (4) Connector API—executes the reusable producer and consumer APIs    that can link the topics to the existing applications.

The Kafka® distributed messaging platform 150 uses Apache Zookeeper 169for, for example, coordination between consumers.

The Kafka® client 161 sends the following message 161A to the Kafka®broker 152:

Producer.send(carX,partitionID), where partitionID=hash(X) %#partitions.

The Kafka® client 162 sends the following message 162A to the Kafka®broker 152:

Consumer. subscribe (topic).

The AFDG 170 includes an agent catalog 172 and regions 114.

The agent client 171 sends a message 171A to a region (e.g., Region 4)of the Spark cluster as follows:

sendMessage(carXagent,regionID), where regionID=hash(X) % #regions). Theagent client 171 can be implemented by one or more respective servers.

The agent catalog 172 stores agent directory information. The agentcatalog 172 can be implemented by one or more respective servers.

In the context of FIG. 1, the partitions 153 can be considered tocorrespond to the Spark Streaming integration for Kafka® distributeddata processing system 186, while the regions 114 can be considered tocorrespond to the AFDG 170.

It is to be noted that the same hash function is used for Kafka®,Spark®, and AFDG.

Hence, an input stream DirectKafkalnputDStream 181 results between thepartitions in the Spark® distributed data processing system 110 and theKafka® distributed messaging platform 150 that is consistent but with nomapping control between the partitions.

FIG. 2 shows an exemplary method 200 for integrating multipledistributed data processing systems with different data partitioning androuting mechanisms, resource sharing policies, and lifecycles into asingle process, in accordance with an embodiment of the presentinvention.

At step 210, (i) use the same partitioning mechanism for Kafka® andAFDG, (ii) create a one-to-one mapping between Kafka® and Spark®partitions, and (iii) start an ADFG server at the beginning ofprocessing each data in a Spark executor if the ADFG server is notstarted (that is, map a Spark® partition to an AFDG region if the Spark®partition corresponding to the Spark® executor is not yet mapped).

In an embodiment, step 210 can include one or more of steps 210A, 210B,and 210C.

At step 210A, divide data into the same number of partitions (orregions) with the same hash function for Kafka® and AFDG.

At step 210B, integrate Kafka® with Spark® Streaming with Kafka® 0.10API for mapping a Kafka® partition to a Spark® partition. For example,in an embodiment, specify LocationStrategies.PreferConsistent toguarantee the mapping is unchanged.

At step 210C, at the beginning of processing each data in a Spark®executor, determine whether or not the Spark® executor has alreadystarted an AFDG server. That is, determine whether or not the Spark®partition corresponding to the Spark® executor is mapped.

If so, then proceed to step 210C1. Otherwise (if not), proceed to step210C2.

At step 210C1, process the data.

At step 210C2, calculate a region ID based on the hash function and thetotal number of regions (across all the systems), and start an AFDGserver for the AFDG region using the region corresponding to theregionID.

It is to be appreciated that with Kafka® 0.10 API, we can create aone-to-one mapping between Kafka® and Spark® partitions, but the mappingis arbitrary and there is no way to specify the mapping. By starting anADFG server for an AFDG region (in other words, mapping a Spark®partition to an AFDG region) at the beginning of processing each data ina Spark® executor if it is not started (not mapped), we could makerouting for Kafka® partitions and AFDG regions consistent with nomapping information about Kafka® and Spark® partitions.

FIG. 3 shows another exemplary method 300 for integrating multipledistributed data processing systems with different data partitioning androuting mechanisms, resource sharing policies, and lifecycles into asingle process, in accordance with an embodiment of the presentinvention.

At step 310, separate processes to satisfy the severest limitation aboutresource sharing, and restart processes at the shortest lifecycle.

In an embodiment, step 310 can include one or more of steps 310A, 310B,and 310C.

At step 310A, separate Spark® worker processes for each AFDG server suchthat each of the processes is processed by a different AFDG server.

In an embodiment, step 310A can be accomplished as follows: set thefollowings in spark-env.sh (in case of 8 cores and 32 GB per node)

-   -   SPARK_WORKER_CORES=1    -   SPARK_WORKER_MEMORY=4 g    -   SPARK_WORKER_INSTANCES=8

It is to be appreciated that step 310A solves the problem of multipleAFDG servers being unable to share the same process. It is to be furtherappreciated that the preceding values are illustrative and depend uponthe particular implementation.

At step 310B, separate an AFDG catalog for each AFDG cluster.

It is to be appreciated that step 310B solves the problem of multipleAFDG clusters being unable to share the same AFDG catalog.

At step 310C, restart Spark® worker at each execution of an AFDG server.

In an embodiment, step 310C can be accomplished as follows: restart theentire Spark cluster at each execution of a Spark® job.

It is to be appreciated that step 310C solves the problem of the AFDGserver being unable to reuse the same process.

In general, it is to be appreciated that the solutions to theaforementioned problems are essentially devices at the time ofoperation, and thus have wide applicability.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 4, illustrative cloud computing environment 450 isdepicted. As shown, cloud computing environment 450 includes one or morecloud computing nodes 410 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 454A, desktop computer 454B, laptop computer 454C,and/or automobile computer system 454N may communicate. Nodes 410 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 450 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 454A-Nshown in FIG. 4 are intended to be illustrative only and that computingnodes 410 and cloud computing environment 450 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers providedby cloud computing environment 450 (FIG. 4) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 560 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 561;RISC (Reduced Instruction Set Computer) architecture based servers 562;servers 563; blade servers 564; storage devices 565; and networks andnetworking components 566. In some embodiments, software componentsinclude network application server software 567 and database software568.

Virtualization layer 570 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers571; virtual storage 572; virtual networks 573, including virtualprivate networks; virtual applications and operating systems 574; andvirtual clients 575.

In one example, management layer 580 may provide the functions describedbelow. Resource provisioning 581 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 582provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 583 provides access to the cloud computing environment forconsumers and system administrators. Service level management 584provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 585 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 590 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 591; software development and lifecycle management 592;virtual classroom education delivery 593; data analytics processing 594;transaction processing 595; and integrating multiple distributed dataprocessing serves with different data partitioning and routingmechanisms, resource sharing policies and lifecycles into a singleprocess 596.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as SMALLTALK, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the embodiment is included in at least one embodiment ofthe present invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope of the invention as outlined by the appended claims.Having thus described aspects of the invention, with the details andparticularity required by the patent laws, what is claimed and desiredprotected by Letters Patent is set forth in the appended claims.

What is claimed is:
 1. A computer-implemented method for enablingconsistent data processing by first and second distributed processingsystems having different data partitioning and routing mechanisms suchthat the first distributed processing system is without states and thesecond distributed processing system is with states, the methodcomprising: dividing data in each of the distributed processing systemsinto a same number of partitions based on a same key and a same hashfunction; and mapping partitions between the distributed processingsystems in a one-to-one mapping, wherein said mapping step includes (i)checking if a partition in the first distributed processing system isalready mapped to a partition in the second distributed processingsystem, responsive to receiving a data set for processing by the firstdistributed processing system; and (ii) calculating a partition ID basedon the hash function and a total number of partitions, and dynamicallymapping the partition in the first distributed processing system to thepartition in the second distributed processing system, responsive to thepartition in the first distributed processing system being unmapped tothe partition in the second distributed processing system.
 2. Thecomputer-implemented method of claim 1, wherein said dividing stepcomprises separating processes executed by multiple servers included inthe first distributed processing system such that each of the multipleservers only executes a respective one of the separated processes. 3.The computer-implemented method of claim 1, further comprisingseparating processes processed by multiple servers included in at leastone of the distributed processing systems to satisfy a severest one of aplurality of limitations on resource sharing imposed on the at least oneof the distributed processing systems.
 4. The computer-implementedmethod of claim 1, wherein the distributed processing systems havedifferent lifecycles, and the method further comprises restartingprocesses executed by the distributed processing systems at a shortestone of the different lifecycles.
 5. The computer-implemented method ofclaim 1, wherein said mapping step is performed using an applicationprogramming interface.
 6. The computer-implemented method of claim 1,wherein the second distributed processing system comprises multipleclusters and multiple catalogs, wherein a constraint is imposed on thesecond distributed processing system such that the multiple clusters arerestricted from sharing a same one of the multiple catalogs, and whereinthe method further comprises overcoming the constraint by separating themultiple catalogs such that only a respective one of the multiplecatalogs is used by each of the multiple clusters.
 7. Thecomputer-implemented method of claim 1, wherein the first distributedprocessing system comprises a cluster having multiple workers, whereinthe second distributed processing system comprises at least one server,wherein a constraint is imposed on the second distributed processingsystem such that the at least one server is restricted from reusing asame process, and wherein the method further comprises overcoming theconstraint by restarting the cluster responsive to an execution of a jobby any of the workers.
 8. The computer-implemented method of claim 1,wherein the first and the second distributed processing systems areconfigured such that a number of partitions in the first distributedprocessing system is equal to both a number of partitions in the seconddistributed processing system and a number of executors in the firstdistributed processing system.
 9. The computer-implemented method ofclaim 1, wherein the first distributed processing system comprises atleast one cluster with a plurality of workers and a plurality ofpartitions, wherein each of the plurality of workers is restricted tousing only a single respective one of the plurality of partitions. 10.The computer-implemented method of claim 1, wherein the partition in thefirst distributed processing system is fixed but unspecified prior tosaid mapping step.
 11. A computer program product for enablingconsistent data processing by first and second distributed processingsystems having different data partitioning and routing mechanisms suchthat the first distributed processing system is without states and thesecond distributed processing system is with states, the computerprogram product comprising a non-transitory computer readable storagemedium having program instructions embodied therewith, the programinstructions executable by a computer to cause the computer to perform amethod comprising: dividing data in each of the distributed processingsystems into a same number of partitions based on a same key and a samehash function; and mapping partitions between the distributed processingsystems in a one-to-one mapping, wherein said mapping step includes (i)checking if a partition in the first distributed processing system isalready mapped to a partition in the second distributed processingsystem, responsive to receiving a data set for processing by the firstdistributed processing system; and (ii) calculating a partition ID basedon the hash function and a total number of partitions, and dynamicallymapping the partition in the first distributed processing system to thepartition in the second distributed processing system, responsive to thepartition in the first distributed processing system being unmapped tothe partition in the second distributed processing system.
 12. Thecomputer program product of claim 11, wherein said dividing stepcomprises separating processes executed by multiple servers included inthe first distributed processing system such that each of the multipleservers only executes a respective one of the separated processes. 13.The computer program product of claim 11, wherein the method furthercomprises separating processes processed by multiple servers included inat least one of the distributed processing systems to satisfy a severestone of a plurality of limitations on resource sharing imposed on the atleast one of the distributed processing systems.
 14. The computerprogram product of claim 11, wherein the distributed processing systemshave different lifecycles, and the method further comprises restartingprocesses executed by the distributed processing systems at a shortestone of the different lifecycles.
 15. The computer program product ofclaim 11, wherein said mapping step is performed using an applicationprogramming interface.
 16. The computer program product of claim 11,wherein the second distributed processing system comprises multipleclusters and multiple catalogs, wherein a constraint is imposed on thesecond distributed processing system such that the multiple clusters arerestricted from sharing a same one of the multiple catalogs, and whereinthe method further comprises overcoming the constraint by separating themultiple catalogs such that only a respective one of the multiplecatalogs is used by each of the multiple clusters.
 17. The computerprogram product of claim 11, wherein the first distributed processingsystem comprises a cluster having multiple workers, wherein the seconddistributed processing system comprises at least one server, wherein aconstraint is imposed on the second distributed processing system suchthat the at least one server is restricted from reusing a same process,and wherein the method further comprises overcoming the constraint byrestarting the cluster responsive to an execution of a job by any of theworkers.
 18. The computer program product of claim 11, wherein the firstand the second distributed processing systems are configured such that anumber of partitions in the first distributed processing system is equalto both a number of partitions in the second distributed processingsystem and a number of executors in the first distributed processingsystem.
 19. The computer program product of claim 11, wherein the firstdistributed processing system comprises at least one cluster with aplurality of workers and a plurality of partitions, wherein each of theplurality of workers is restricted to using only a single respective oneof the plurality of partitions.
 20. The computer program product ofclaim 11, wherein the partition in the first distributed processingsystem is fixed but unspecified prior to said mapping step.